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Comparative Study of Different MCDA-Based Approaches in Sustainable Supplier Selection Problem

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Book cover Information Technology for Management: Emerging Research and Applications (AITM 2018, ISM 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 346))

Abstract

One of the crucial aspects of maintaining the business continuity of a company is the process of sustainable supplier selection. If the suppliers are chosen improperly, a slow-down or even a complete suspension of the operations within a company can occur. In this paper, a new unique approach is presented in which the classical MCDA paradigm is extended with aspects of temporal evaluation. In practical terms, three popular MCDA methods are used to evaluate suppliers based on data obtained from five temporal aggregation strategies. The combination of three MCDA methods (namely AHP, TOPSIS and COMET) allows to obtain a rank-reversal-free solution with a reference model and well defined hierarchical structure of the problem. Moreover, the rankings obtained from the MCDA methods are then aggregated to provide the Decision Maker a single ranking of suppliers. The effect of each temporal aggregation strategy on the eventual selection of the winning supplier is also studied.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology in Szczecin, Żołnierska 49, 71-210, Szczecin, Poland

    Artur Karczmarczyk & Jarosław Jankowski

  2. Faculty of Economics and Management, University of Szczecin, Mickewicza 64, 71-101, Szczecin, Poland

    Jarosław Wątróbski

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  1. Artur Karczmarczyk
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  2. Jarosław Wątróbski
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  3. Jarosław Jankowski
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Correspondence to Jarosław Wątróbski .

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  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

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Karczmarczyk, A., Wątróbski, J., Jankowski, J. (2019). Comparative Study of Different MCDA-Based Approaches in Sustainable Supplier Selection Problem. In: Ziemba, E. (eds) Information Technology for Management: Emerging Research and Applications. AITM ISM 2018 2018. Lecture Notes in Business Information Processing, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-15154-6_10

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